MODELING RECREATION SITE CHOICE: DO HYPOTHETICAL CHOICES REFLECT ACTUAL BEHAVIOR?
Bibliographic record
Abstract
This study examines the ability of revealed preference (RP), site-specific stated preference (SP), transferred SP, and various joint RP-SP models to predict aggregate and individual recreation site choice behavior in a holdout sample. For two statistical comparisons, the site-specific RP model provided the most accurate predictions of individual choices. However, the transferred SP model, applied directly or estimated jointly with the RP data, performed best in three aggregate and one individual prediction tests and second best in the other individual prediction comparisons. In every test examined the transferred SP models outperformed the site-specific SP models. This result is traced to the method used to collect the hypothetical choice data (mail out vs. in-person settings) and illustrates the importance of data quality in accuracy of behavioral prediction. These findings suggest that data from well designed and conducted SP surveys from one site can be combined with site-specific RP data from another site to generate improved models of recreation site choice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".